This article reviews the gradient sampling methodology for solving nonsmooth, nonconvex
optimization problems. We state an intuitively straightforward gradient sampling algorithm …

Dynamical systems are at the core of computational models for a wide range of complex
phenomena and, as a consequence, the simulation of dynamical systems has become a …

We propose an algorithm for solving nonsmooth, nonconvex, constrained optimization
problems as well as a new set of visualization tools for comparing the performance of …

The empirical success of derivative-free methods in reinforcement learning for planning
through contact seems at odds with the perceived fragility of classical gradient-based …

Managing uncertainty and variability in power injections has become a major concern for
power system operators due to increasing levels of fluctuating renewable energy connected …

The human perception of stylistic similarity transcends structure and function: for instance, a
bed and a dresser may share a common style. An algorithmically computed style similarity …

We introduce a new method for solving nonlinear continuous optimization problems with
chance constraints. Our method is based on a reformulation of the probabilistic constraint as …

In this paper, we focus on the nonconvex-strongly-convex bilevel optimization problem
(BLO). In this BLO, the objective function of the upper-level problem is nonconvex and …

A line search algorithm for minimizing nonconvex and/or nonsmooth objective functions is
presented. The algorithm is a hybrid between a standard Broyden–Fletcher–Goldfarb …

Small-Signal Stability Constrained Optimal Power Flow (SSSC-OPF) can provide additional
stability measures and control strategies to guarantee the system to be small-signal stable …